2023-03-29 米国国立標準技術研究所(NIST)
米国国立標準技術研究所（NIST）の研究者たちは、100年以上のハリケーンデータと現代のAI技術を備え、ハリケーンのデジタルシミュレーションの新しい方法を考案しました。この手法により、ハリケーンが建物に与える影響をより正確に予測するための新しい設計指針を開発することができる可能性があります。
＜関連情報＞
- https://www.nist.gov/news-events/news/2023/03/ai-could-set-new-bar-designing-hurricane-resistant-buildings
- https://journals.ametsoc.org/view/journals/aies/aop/AIES-D-22-0060.1/AIES-D-22-0060.1.xml
大西洋のハリケーンコースと特徴のシミュレーション： 機械学習と連動したアプローチ Simulation of Atlantic Hurricane Tracks and Features: A Coupled Machine Learning Approach
Rikhi Bose,Adam L. Pintar and Emil Simiu
Artificial Intelligence for the Earth Systems Published:27 Mar 2023
DOI:https://doi.org/10.1175/AIES-D-22-0060.1
Abstract
The objective of this paper is to employ machine learning (ML) and deep learning (DL) techniques to obtain, from input data (storm features) available in or derived from the HURDAT2 database, models capable of simulating important hurricane properties (e.g., landfall location and wind speed) consistent with historical records. In pursuit of this objective, a trajectory model providing the storm center in terms of longitude and latitude, and intensity models providing the central pressure and maximum 1–min wind speed at 10m elevationwere created. The trajectory and intensity models are coupled and must be advanced together, six hours at a time, as the features that serve as inputs to the models at any given step depend on predictions at the previous time steps. Once a synthetic storm database is generated, properties of interest, such as the frequencies of large wind speeds may be extracted from any part of the simulation domain. The coupling of the trajectory and intensity models obviates the need for an intensity decay model inland of the coastline. Prediction results are compared to historical data, and the efficacy of the storm simulation models is evaluated at four sites: New Orleans, Miami, Cape Hatteras, and Boston.
